3523a61256c3040aa3f4fd3fc579511f85169212,tests/conftest.py,,_model_1,#Any#,97

Before Change

    x_train, y_train, x_test, y_test = _dataset

    input_shape = x_train.shape[1:]
    input_layer = Input(input_shape)

    layer = Conv2D(filters=16,
                   kernel_size=(5, 5),
                   strides=(2, 2),
                   activation="relu",
                   use_bias=False)(input_layer)
    layer = Conv2D(filters=32,
                   kernel_size=(3, 3),
                   activation="relu",
                   use_bias=False)(layer)
    layer = AveragePooling2D()(layer)
    layer = Conv2D(filters=8,
                   kernel_size=(3, 3),
                   padding="same",
                   activation="relu",
                   use_bias=False)(layer)
    layer = Flatten()(layer)
    layer = Dropout(0.01)(layer)
    layer = Dense(units=10,
                  activation="softmax",
                  use_bias=False)(layer)

    model = Model(input_layer, layer)

    model.compile("adam", "categorical_crossentropy", ["accuracy"])

    history = model.fit(x_train, y_train, batch_size=64, epochs=1, verbose=2,
                        validation_data=(x_test, y_test))

    assert history.history["val_accuracy"][-1] > 0.95

    return model


@pytest.fixture(scope="session")
def _model_2(_dataset):

After Change

@pytest.fixture(scope="session")
def _model_1(_dataset):
    return get_model_1(_dataset)


def get_model_1(dataset):
    x_train, y_train, x_test, y_test = dataset

Instances